Standard

Investigating Type Declaration Mismatches in Python. / Pascarella, Luca; Ram, Achyudh ; Nadeem, Azqa ; Bisesser, Dinesh; Knyazev, Norman; Bacchelli, Alberto.

Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE). 2018.

Research output: Scientific - peer-reviewConference contribution

Harvard

Pascarella, L, Ram, A, Nadeem, A, Bisesser, D, Knyazev, N & Bacchelli, A 2018, Investigating Type Declaration Mismatches in Python. in Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE).

APA

Pascarella, L., Ram, A., Nadeem, A., Bisesser, D., Knyazev, N., & Bacchelli, A. (2018). Investigating Type Declaration Mismatches in Python. In Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE)

Vancouver

Pascarella L, Ram A, Nadeem A, Bisesser D, Knyazev N, Bacchelli A. Investigating Type Declaration Mismatches in Python. In Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE). 2018.

Author

Pascarella, Luca ; Ram, Achyudh ; Nadeem, Azqa ; Bisesser, Dinesh ; Knyazev, Norman ; Bacchelli, Alberto. / Investigating Type Declaration Mismatches in Python. Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE). 2018.

BibTeX

@inbook{0be4cec6ca0a46a386b31bb5550577a7,
title = "Investigating Type Declaration Mismatches in Python",
abstract = "Past research provided evidence that developers making code changes sometimes omit to update the related documentation, thus creating inconsistencies that may contribute to faults and crashes. In dynamically typed languages, such as Python, an inconsistency in the documentation may lead to a mismatch in type declarations only visible at runtime.With our study, we investigate how often the documentation is inconsistent in a sample of 239 methods from five Python open- source software projects. Our results highlight that more than 20% of the comments are either partially defined or entirely missing and that almost 1% of the methods in the analyzed projects contain type inconsistencies. Based on these results, we create a tool, PyID, to early detect type mismatches in Python documentation and we evaluate its performance with our oracle.",
author = "Luca Pascarella and Achyudh Ram and Azqa Nadeem and Dinesh Bisesser and Norman Knyazev and Alberto Bacchelli",
year = "2018",
booktitle = "Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE)",

}

RIS

TY - CHAP

T1 - Investigating Type Declaration Mismatches in Python

AU - Pascarella,Luca

AU - Ram,Achyudh

AU - Nadeem,Azqa

AU - Bisesser,Dinesh

AU - Knyazev,Norman

AU - Bacchelli,Alberto

PY - 2018

Y1 - 2018

N2 - Past research provided evidence that developers making code changes sometimes omit to update the related documentation, thus creating inconsistencies that may contribute to faults and crashes. In dynamically typed languages, such as Python, an inconsistency in the documentation may lead to a mismatch in type declarations only visible at runtime.With our study, we investigate how often the documentation is inconsistent in a sample of 239 methods from five Python open- source software projects. Our results highlight that more than 20% of the comments are either partially defined or entirely missing and that almost 1% of the methods in the analyzed projects contain type inconsistencies. Based on these results, we create a tool, PyID, to early detect type mismatches in Python documentation and we evaluate its performance with our oracle.

AB - Past research provided evidence that developers making code changes sometimes omit to update the related documentation, thus creating inconsistencies that may contribute to faults and crashes. In dynamically typed languages, such as Python, an inconsistency in the documentation may lead to a mismatch in type declarations only visible at runtime.With our study, we investigate how often the documentation is inconsistent in a sample of 239 methods from five Python open- source software projects. Our results highlight that more than 20% of the comments are either partially defined or entirely missing and that almost 1% of the methods in the analyzed projects contain type inconsistencies. Based on these results, we create a tool, PyID, to early detect type mismatches in Python documentation and we evaluate its performance with our oracle.

M3 - Conference contribution

BT - Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE)

ER -

ID: 40300814